# Spectrum Monitoring for Radar Bands using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1705.00462 · HTML: https://mlsanity.com/paper/1705.00462/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2017.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Realtime Spectrum Monitoring via Reinforcement Learning -- A Comparison Between Q-Learning and Heuristic Methods](https://mlsanity.com/paper/2307.05763/) (2023)
- [ZoomSpec: A Physics-Guided Coarse-to-Fine Framework for Wideband Spectrum Sensing](https://mlsanity.com/paper/2604.13568/) (2026)

## Abstract (arXiv)

In this paper, we present a spectrum monitoring framework for the detection of radar signals in spectrum sharing scenarios. The core of our framework is a deep convolutional neural network (CNN) model that enables Measurement Capable Devices to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial LTE and WLAN. We collected a large dataset of RF measurements, which include the transmissions of multiple radar pulse waveforms, downlink LTE, WLAN, and thermal noise. We propose a pre-processing data representation that leverages the amplitude and phase shifts of the collected samples. This representation allows our CNN model to achieve a classification accuracy of 99.6% on our testing dataset. The trained CNN model is then tested under various SNR values, outperforming other models, such as spectrogram-based CNN models.